
import warnings
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px

from glob import glob
from category_encoders import OneHotEncoder
from ipywidgets import Dropdown, FloatSlider, IntSlider, interact
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error
from sklearn.pipeline import make_pipeline

warnings.simplefilter(action="ignore", category=FutureWarning)


df1 = pd.read_csv("data-1.csv", encoding="ISO-8859-1")


df1.shape

(8606, 16)


df1.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8606 entries, 0 to 8605
Data columns (total 16 columns):
 #   Column                      Non-Null Count  Dtype  
---  ------                      --------------  -----  
 0   operation                   8606 non-null   object 
 1   property_type               8606 non-null   object 
 2   place_with_parent_names     8606 non-null   object 
 3   lat-lon                     6936 non-null   object 
 4   price                       7590 non-null   float64
 5   currency                    7590 non-null   object 
 6   price_aprox_local_currency  7590 non-null   float64
 7   price_aprox_usd             7590 non-null   float64
 8   surface_total_in_m2         5946 non-null   float64
 9   surface_covered_in_m2       7268 non-null   float64
 10  price_usd_per_m2            4895 non-null   float64
 11  price_per_m2                6520 non-null   float64
 12  floor                       1259 non-null   float64
 13  rooms                       4752 non-null   float64
 14  expenses                    875 non-null    object 
 15  properati_url               8606 non-null   object 
dtypes: float64(9), object(7)
memory usage: 1.1+ MB


df1.head()


def wrangle(filepath):
    
    """
    Read CSV files into a 'DataFrame'.

    Returns only property apartments in Capital Federal which the price is less than 400 000 USD.

    Parameters
    ----------
    filepath : str
        Location of CSV file.
    """
    
    # Import CSV
    df = pd.read_csv(filepath, encoding="ISO-8859-1")

    # Subset to properties in 'Capital Federal'
    mask_ba = df["place_with_parent_names"].str.contains("Capital Federal")

    # Subset to 'apartments'
    mask_apt = df["property_type"] == "apartment"

    # Subset to properties where price is less than 400 000
    mask_price = df["price_aprox_usd"] < 400_000

    # Applying all masks
    df = df[mask_ba & mask_apt & mask_price]

    # Return new Data Frame
    return df


df = wrangle("data-1.csv")
df.shape

(1781, 16)


# Plotting a histogram related to the price per m2
plt.hist(df["surface_covered_in_m2"])

# Detailing title and axis, ';' to supress any text output
plt.title("Distribution of Apartment Sizes")
plt.xlabel("Area [sq meters]")
plt.ylabel("Frequency");


df.describe()


df.describe().apply(lambda s: s.apply('{0:.5f}'.format))


def wrangle(filepath):
    
    """
    Read CSV files into a 'DataFrame'.

    Returns only property apartments in Capital Federal which the price is less than 400 000 USD.

    Parameters
    ----------
    filepath : str
        Location of CSV file.
    """
    
    # Import CSV
    df = pd.read_csv(filepath, encoding="ISO-8859-1")

    # Subset to properties in 'Capital Federal'
    mask_ba = df["place_with_parent_names"].str.contains("Capital Federal")

    # Subset to 'apartments'
    mask_apt = df["property_type"] == "apartment"

    # Subset to properties where price is less than 400 000
    mask_price = df["price_aprox_usd"] < 400_000

    # Applying all masks
    df = df[mask_ba & mask_apt & mask_price]
    
    # Remove outliers by "surface_covered_in_m2"
    low, high = df["surface_covered_in_m2"].quantile([0.1, 0.9])
    mask_area = df["surface_covered_in_m2"].between(low, high)
    df = df[mask_area]

    # Return new Data Frame
    return df


df = wrangle("data-1.csv")
df.shape

(1343, 16)


# Plotting a histogram related to the price per m2
plt.hist(df["surface_covered_in_m2"])

# Detailing title and axis, ';' to supress any text output
plt.title("Distribution of Apartment Sizes")
plt.xlabel("Area [sq meters]")
plt.ylabel("Frequency");


# Plotting a scatter plot
plt.scatter(df["surface_covered_in_m2"], df["price_aprox_usd"])

# Detailing title and axis, ';' to supress any text output
plt.title(" Area X Price")
plt.xlabel("Area [sq meters]")
plt.ylabel("Price [USD]")

Text(0, 0.5, 'Price [USD]')


def wrangle(filepath):
    
    """
    Read CSV files into a 'DataFrame'.

    Returns only property apartments in Capital Federal which the price is less than 400 000 USD.

    Parameters
    ----------
    filepath : str
        Location of CSV file.
    """
    
    # Import CSV
    df = pd.read_csv(filepath, encoding="ISO-8859-1")

    # Subset to properties in 'Capital Federal'
    mask_ba = df["place_with_parent_names"].str.contains("Capital Federal")

    # Subset to 'apartments'
    mask_apt = df["property_type"] == "apartment"

    # Subset to properties where price is less than 400 000
    mask_price = df["price_aprox_usd"] < 400_000

    # Applying all masks
    df = df[mask_ba & mask_apt & mask_price]
    
    # Remove outliers by "surface_covered_in_m2"
    low, high = df["surface_covered_in_m2"].quantile([0.1, 0.9])
    mask_area = df["surface_covered_in_m2"].between(low, high)
    df = df[mask_area]
    
    # Split the "lat-lon" col
    df[["lat", "lon"]] = df["lat-lon"].str.split(",", expand=True).astype(float)
    df.drop(columns="lat-lon", inplace=True)

    # Return new Data Frame
    return df


df = wrangle("data-1.csv")
df.head()


fig = px.scatter_mapbox(
    df,  # DataFrame
    lat='lat',
    lon='lon',
    width=600,  # Width of map
    height=600,  # Height of map
    color='price_aprox_usd',
    hover_data=["price_aprox_usd"],  # Display price when hovering mouse over house
)

fig.update_layout(mapbox_style="open-street-map")

fig.show()


files = glob("data-*.csv")
files

['data\\data-1.csv',
 'data\\data-2.csv',
 'data\\data-3.csv',
 'data\\data-4.csv',
 'data\\data-5.csv']


frames = []
for file in files:
    frames.append(wrangle(file))
len(frames)

5


df = pd.concat(frames)
df.head()


fig = px.scatter_mapbox(
    df,  # DataFrame
    lat='lat',
    lon='lon',
    width=600,  # Width of map
    height=600,  # Height of map
    color='price_aprox_usd',
    hover_data=["price_aprox_usd"],  # Display price when hovering mouse over house
)

fig.update_layout(mapbox_style="open-street-map")

fig.show()


def wrangle(filepath):
    
    """
    Read CSV files into a 'DataFrame'.

    Returns only property apartments in Capital Federal which the price is less than 400 000 USD.

    Parameters
    ----------
    filepath : str
        Location of CSV file.
    """
    
    # Import CSV
    df = pd.read_csv(filepath, encoding="ISO-8859-1")

    # Subset to properties in 'Capital Federal'
    mask_ba = df["place_with_parent_names"].str.contains("Capital Federal")

    # Subset to 'apartments'
    mask_apt = df["property_type"] == "apartment"

    # Subset to properties where price is less than 400 000
    mask_price = df["price_aprox_usd"] < 400_000

    # Applying all masks
    df = df[mask_ba & mask_apt & mask_price]
    
    # Remove outliers by "surface_covered_in_m2"
    low, high = df["surface_covered_in_m2"].quantile([0.1, 0.9])
    mask_area = df["surface_covered_in_m2"].between(low, high)
    df = df[mask_area]
    
    # Split the "lat-lon" col
    df[["lat", "lon"]] = df["lat-lon"].str.split(",", expand=True).astype(float)
    df.drop(columns="lat-lon", inplace=True)
    
    # Creating a neighboorhood column
    df["neighborhood"] = df["place_with_parent_names"].str.split('|', 0, expand=True)[3]

    # Return new Data Frame
    return df


df = wrangle("data-1.csv")
df.head()


df.groupby("neighborhood")[["price", "surface_covered_in_m2"]].mean().sort_values(by="price", ascending=False)


df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1343 entries, 4 to 8604
Data columns (total 18 columns):
 #   Column                      Non-Null Count  Dtype  
---  ------                      --------------  -----  
 0   operation                   1343 non-null   object 
 1   property_type               1343 non-null   object 
 2   place_with_parent_names     1343 non-null   object 
 3   price                       1343 non-null   float64
 4   currency                    1343 non-null   object 
 5   price_aprox_local_currency  1343 non-null   float64
 6   price_aprox_usd             1343 non-null   float64
 7   surface_total_in_m2         965 non-null    float64
 8   surface_covered_in_m2       1343 non-null   float64
 9   price_usd_per_m2            927 non-null    float64
 10  price_per_m2                1343 non-null   float64
 11  floor                       379 non-null    float64
 12  rooms                       1078 non-null   float64
 13  expenses                    349 non-null    object 
 14  properati_url               1343 non-null   object 
 15  lat                         1300 non-null   float64
 16  lon                         1300 non-null   float64
 17  neighborhood                1343 non-null   object 
dtypes: float64(11), object(7)
memory usage: 199.4+ KB


df.isnull().sum() / len(df)

operation                     0.000000
property_type                 0.000000
place_with_parent_names       0.000000
price                         0.000000
currency                      0.000000
price_aprox_local_currency    0.000000
price_aprox_usd               0.000000
surface_total_in_m2           0.281459
surface_covered_in_m2         0.000000
price_usd_per_m2              0.309754
price_per_m2                  0.000000
floor                         0.717796
rooms                         0.197319
expenses                      0.740134
properati_url                 0.000000
lat                           0.032018
lon                           0.032018
neighborhood                  0.000000
dtype: float64


def wrangle(filepath):
    
    """
    Read CSV files into a 'DataFrame'.

    Returns only property apartments in Capital Federal which the price is less than 400 000 USD.

    Parameters
    ----------
    filepath : str
        Location of CSV file.
    """
    
    # Import CSV
    df = pd.read_csv(filepath, encoding="ISO-8859-1")

    # Subset to properties in 'Capital Federal'
    mask_ba = df["place_with_parent_names"].str.contains("Capital Federal")

    # Subset to 'apartments'
    mask_apt = df["property_type"] == "apartment"

    # Subset to properties where price is less than 400 000
    mask_price = df["price_aprox_usd"] < 400_000

    # Applying all masks
    df = df[mask_ba & mask_apt & mask_price]
    
    # Remove outliers by "surface_covered_in_m2"
    low, high = df["surface_covered_in_m2"].quantile([0.1, 0.9])
    mask_area = df["surface_covered_in_m2"].between(low, high)
    df = df[mask_area]
    
    # Split the "lat-lon" col
    df[["lat", "lon"]] = df["lat-lon"].str.split(",", expand=True).astype(float)
    df.drop(columns="lat-lon", inplace=True)
    
    # Creating a neighboorhood column
    df["neighborhood"] = df["place_with_parent_names"].str.split('|', 0, expand=True)[3]
    
    # Drop columns with more than half null values
    df.drop(columns=["floor", "expenses"], inplace=True)

    # Return new Data Frame
    return df


df = wrangle("data-1.csv")
df.head()


# Selecting all columns with objects as type, that is, identifiable as a category column
df.select_dtypes("object").head()


# Counting how many unique objects there are in those columns
df.select_dtypes("object").nunique()

operation                     1
property_type                 1
place_with_parent_names      53
currency                      2
properati_url              1343
neighborhood                 53
dtype: int64


len(df)

1343


def wrangle(filepath):
    
    """
    Read CSV files into a 'DataFrame'.

    Returns only property apartments in Capital Federal which the price is less than 400 000 USD.

    Parameters
    ----------
    filepath : str
        Location of CSV file.
    """
    
    # Import CSV
    df = pd.read_csv(filepath, encoding="ISO-8859-1")

    # Subset to properties in 'Capital Federal'
    mask_ba = df["place_with_parent_names"].str.contains("Capital Federal")

    # Subset to 'apartments'
    mask_apt = df["property_type"] == "apartment"

    # Subset to properties where price is less than 400 000
    mask_price = df["price_aprox_usd"] < 400_000

    # Applying all masks
    df = df[mask_ba & mask_apt & mask_price]
    
    # Remove outliers by "surface_covered_in_m2"
    low, high = df["surface_covered_in_m2"].quantile([0.1, 0.9])
    mask_area = df["surface_covered_in_m2"].between(low, high)
    df = df[mask_area]
    
    # Split the "lat-lon" col
    df[["lat", "lon"]] = df["lat-lon"].str.split(",", expand=True).astype(float)
    df.drop(columns="lat-lon", inplace=True)
    
    # Creating a neighboorhood column
    df["neighborhood"] = df["place_with_parent_names"].str.split('|', 0, expand=True)[3]
    
    # Drop columns with more than half null values
    df.drop(columns=["floor", "expenses"], inplace=True)
    
    # Drop low and high cardinality categorical variables
    df.drop(columns=["operation", "property_type", "currency", "properati_url"], inplace=True)

    # Return new Data Frame
    return df


df = wrangle("data-1.csv")
df.head()


df.columns.to_list()

['place_with_parent_names',
 'price',
 'price_aprox_local_currency',
 'price_aprox_usd',
 'surface_total_in_m2',
 'surface_covered_in_m2',
 'price_usd_per_m2',
 'price_per_m2',
 'rooms',
 'lat',
 'lon',
 'neighborhood']


def wrangle(filepath):
    
    """
    Read CSV files into a 'DataFrame'.

    Returns only property apartments in Capital Federal which the price is less than 400 000 USD.

    Parameters
    ----------
    filepath : str
        Location of CSV file.
    """
    
    # Import CSV
    df = pd.read_csv(filepath, encoding="ISO-8859-1")

    # Subset to properties in 'Capital Federal'
    mask_ba = df["place_with_parent_names"].str.contains("Capital Federal")

    # Subset to 'apartments'
    mask_apt = df["property_type"] == "apartment"

    # Subset to properties where price is less than 400 000
    mask_price = df["price_aprox_usd"] < 400_000

    # Applying all masks
    df = df[mask_ba & mask_apt & mask_price]
    
    # Remove outliers by "surface_covered_in_m2"
    low, high = df["surface_covered_in_m2"].quantile([0.1, 0.9])
    mask_area = df["surface_covered_in_m2"].between(low, high)
    df = df[mask_area]
    
    # Split the "lat-lon" col
    df[["lat", "lon"]] = df["lat-lon"].str.split(",", expand=True).astype(float)
    df.drop(columns="lat-lon", inplace=True)
    
    # Creating a neighboorhood column
    df["neighborhood"] = df["place_with_parent_names"].str.split('|', 0, expand=True)[3]
    
    # Drop columns with more than half null values
    df.drop(columns=["floor", "expenses"], inplace=True)
    
    # Drop low and high cardinality categorical variables
    df.drop(columns=["operation", "property_type", "currency", "properati_url"], inplace=True)
    
    # Drop leakage 
    df.drop(columns=['price', 'price_aprox_local_currency', 'price_per_m2', 'price_usd_per_m2'], inplace=True)

    # Return new Data Frame
    return df


df = wrangle("data-1.csv")
df.head()


# Get the correlation between all number type columns
corr = df.select_dtypes("number").drop(columns="price_aprox_usd").corr()

# Plot the heatmap
sns.heatmap(corr);


df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1343 entries, 4 to 8604
Data columns (total 8 columns):
 #   Column                   Non-Null Count  Dtype  
---  ------                   --------------  -----  
 0   place_with_parent_names  1343 non-null   object 
 1   price_aprox_usd          1343 non-null   float64
 2   surface_total_in_m2      965 non-null    float64
 3   surface_covered_in_m2    1343 non-null   float64
 4   rooms                    1078 non-null   float64
 5   lat                      1300 non-null   float64
 6   lon                      1300 non-null   float64
 7   neighborhood             1343 non-null   object 
dtypes: float64(6), object(2)
memory usage: 94.4+ KB


def wrangle(filepath):
    
    """
    Read CSV files into a 'DataFrame'.

    Returns only property apartments in Capital Federal which the price is less than 400 000 USD.

    Parameters
    ----------
    filepath : str
        Location of CSV file.
    """
    
    # Import CSV
    df = pd.read_csv(filepath, encoding="ISO-8859-1")

    # Subset to properties in 'Capital Federal'
    mask_ba = df["place_with_parent_names"].str.contains("Capital Federal")

    # Subset to 'apartments'
    mask_apt = df["property_type"] == "apartment"

    # Subset to properties where price is less than 400 000
    mask_price = df["price_aprox_usd"] < 400_000

    # Applying all masks
    df = df[mask_ba & mask_apt & mask_price]
    
    # Remove outliers by "surface_covered_in_m2"
    low, high = df["surface_covered_in_m2"].quantile([0.1, 0.9])
    mask_area = df["surface_covered_in_m2"].between(low, high)
    df = df[mask_area]
    
    # Split the "lat-lon" col
    df[["lat", "lon"]] = df["lat-lon"].str.split(",", expand=True).astype(float)
    df.drop(columns="lat-lon", inplace=True)
    
    # Creating a neighboorhood column
    df["neighborhood"] = df["place_with_parent_names"].str.split('|', 0, expand=True)[3]
    
    # Drop columns with more than half null values
    df.drop(columns=["floor", "expenses"], inplace=True)
    
    # Drop low and high cardinality categorical variables
    df.drop(columns=["operation", "property_type", "currency", "properati_url"], inplace=True)
    
    # Drop leakage 
    df.drop(columns=['price', 'price_aprox_local_currency', 'price_per_m2', 'price_usd_per_m2'], inplace=True)
    
    # Drop columns with multicollinearlity
    df.drop(columns=["surface_total_in_m2", "rooms"], inplace=True)

    # Return new Data Frame
    return df


frames = []
for file in files:
    frames.append(wrangle(file))
df = pd.concat(frames)
df.head()


# The target is the aproximate price
target = "price_aprox_usd"

# The feature matrix is the rest of the DataFrame
X_train = df[['surface_covered_in_m2', 'lat', 'lon', 'neighborhood']]
y_train = df[target]


# The overall mean will the the baseline
y_mean = y_train.mean()

# One value for each row
y_pred_baseline = [y_mean] * len(y_train)
print("Mean apt price:", round(y_mean, 2))

print("Baseline MAE:", round(mean_absolute_error(y_train, y_pred_baseline), 2))

Mean apt price: 132383.84
Baseline MAE: 44860.11


df.neighborhood.nunique()

57


model = make_pipeline(OneHotEncoder(use_cat_names=True), SimpleImputer(), LinearRegression())
model.fit(X_train, y_train)

Pipeline(steps=[('onehotencoder',
                 OneHotEncoder(cols=['neighborhood'], use_cat_names=True)),
                ('simpleimputer', SimpleImputer()),
                ('linearregression', LinearRegression())])


intercept = model.named_steps['linearregression'].intercept_
coefficients = model.named_steps['linearregression'].coef_
print("coefficients len:", len(coefficients))
print(coefficients[:5])  # First five coefficients

coefficients len: 60
[  2208.54913208 247000.81749086 148752.4894247     561.92516784
  13068.13103889]


# Predicting values
y_pred_training = model.predict(X_train)

# How good is its MAE
print("Training MAE:", round(mean_absolute_error(y_train, y_pred_training), 2))

Training MAE: 24237.29


def make_prediction(area, lat, lon, neighborhood):
    
    """
    Input the area, latitude, longitude and neighborhood of a house into the Linear Regression model

    Returns the apartment predicted price

    Parameters
    ----------
    area: float
        The size of the house in square feet
    
    lat: float
        Latitude of the house
        
    lon: float
        Longitude of the house
        
    neighborhood: str
        Neighborhood of the house, where it is located
    """
    
    # Create a dictionary with the received info
    data = {
        "surface_covered_in_m2": area,
        "lat": lat,
        "lon": lon,
        "neighborhood": neighborhood
    }
    
    # Create a DataFrame with the dictionary
    df = pd.DataFrame(data, index=[0])
    
    # Make a prediction
    prediction = model.predict(df).round(2)[0]
    
    # Return the predicted value
    return f"Predicted apartment price: ${prediction}"


# Using the fuction for the first time
make_prediction(110, -34.60, -58.46, "Villa Crespo")

'Predicted apartment price: $248868.18'


# Invoke the interact method
interact(
    make_prediction, # Pass it the predict function
    area=IntSlider( # Start and end of the area slider
        min=X_train["surface_covered_in_m2"].min(),
        max=X_train["surface_covered_in_m2"].max(),
        value=X_train["surface_covered_in_m2"].mean(),
    ),
    lat=FloatSlider( # Start and end of the latitude slider
        min=X_train["lat"].min(),
        max=X_train["lat"].max(),
        step=0.01,
        value=X_train["lat"].mean(),
    ),
    lon=FloatSlider(  # Start and end of the longitude slider
        min=X_train["lon"].min(),
        max=X_train["lon"].max(),
        step=0.01,
        value=X_train["lon"].mean(),
    ),
    neighborhood=Dropdown(options=sorted(X_train["neighborhood"].unique())), # Dropdown with all the neighborhoods
);

interactive(children=(IntSlider(value=53, description='area', max=101, min=30), FloatSlider(value=-34.59890626…


# Get the model coeficients
coefficients = model.named_steps['linearregression'].coef_


# Get feature names
feature_names = model.named_steps["onehotencoder"].get_feature_names() 

# How many features
print("features len:", len(feature_names)) 
print(feature_names[:5])

features len: 60
['surface_covered_in_m2', 'lat', 'lon', 'neighborhood_Chacarita', 'neighborhood_Villa Luro']


# Transform list into Series
feat_imp = pd.Series(coefficients, index=feature_names) 
feat_imp.head()

surface_covered_in_m2        2208.549132
lat                        247000.817491
lon                        148752.489425
neighborhood_Chacarita        561.925168
neighborhood_Villa Luro     13068.131039
dtype: float64


# Plot the features with the highest importances
feat_imp.sort_values(key=abs).tail(15).plot(kind='barh')

# Plot and update
plt.xlabel('Importance [USD]')
plt.ylabel('Feature')
plt.title('Feature Importance for Apartment Price')
plt.show()

	operation	property_type	place_with_parent_names	lat-lon	price	currency	price_aprox_local_currency	price_aprox_usd	surface_total_in_m2	surface_covered_in_m2	price_usd_per_m2	price_per_m2	floor	rooms	expenses	properati_url
0	sell	apartment	\|Argentina\|Capital Federal\|Villa Crespo\|	-34.6047834183,-58.4586812499	180000.0	USD	2729232.0	180000.0	120.0	110.0	1500.000000	1636.363636	NaN	4.0	NaN	http://villa-crespo.properati.com.ar/12egq_ven...
1	sell	house	\|Argentina\|Bs.As. G.B.A. Zona Oeste\|La Matanza...	NaN	250000.0	USD	3790600.0	250000.0	117.0	120.0	2136.752137	2083.333333	NaN	4.0	NaN	http://ramos-mejia.properati.com.ar/s7pd_venta...
2	sell	house	\|Argentina\|Bs.As. G.B.A. Zona Oeste\|Morón\|Cast...	-34.6497002,-58.658073	410000.0	USD	6216584.0	410000.0	410.0	220.0	1000.000000	1863.636364	NaN	NaN	NaN	http://castelar-moron.properati.com.ar/11vgn_v...
3	sell	house	\|Argentina\|Bs.As. G.B.A. Zona Oeste\|Tres de Fe...	-34.5957086,-58.5669503	180000.0	USD	2729232.0	180000.0	200.0	135.0	900.000000	1333.333333	NaN	5.0	NaN	http://tres-de-febrero.properati.com.ar/7f7u_v...
4	sell	apartment	\|Argentina\|Capital Federal\|Chacarita\|	-34.5846508988,-58.4546932614	129000.0	USD	1955949.6	129000.0	76.0	70.0	1697.368421	1842.857143	NaN	NaN	NaN	http://chacarita.properati.com.ar/10qlv_venta_...

	price	price_aprox_local_currency	price_aprox_usd	surface_total_in_m2	surface_covered_in_m2	price_usd_per_m2	price_per_m2	floor	rooms
count	1.781000e+03	1.781000e+03	1781.000000	1247.000000	1635.000000	1198.000000	1618.000000	482.000000	1385.000000
mean	2.285410e+05	2.178037e+06	143647.215239	114.417001	97.877064	2231.534726	4340.345113	10.085062	2.434657
std	4.100189e+05	1.141710e+06	75298.758506	1755.263895	1533.057610	650.139587	8126.940810	44.783320	1.065193
min	0.000000e+00	0.000000e+00	0.000000	0.000000	0.000000	0.000000	2.224587	1.000000	1.000000
25%	9.000000e+04	1.349454e+06	89000.000000	40.000000	38.000000	1815.485830	1973.750000	2.000000	2.000000
50%	1.300000e+05	1.849813e+06	122000.000000	55.000000	50.000000	2151.642336	2411.239496	4.000000	2.000000
75%	1.911340e+05	2.653420e+06	175000.000000	79.000000	73.000000	2577.003583	2958.157462	7.000000	3.000000
max	5.487000e+06	6.034635e+06	398000.000000	62034.000000	62034.000000	5975.609756	57156.250000	616.000000	6.000000

	price	price_aprox_local_currency	price_aprox_usd	surface_total_in_m2	surface_covered_in_m2	price_usd_per_m2	price_per_m2	floor	rooms
count	1781.00000	1781.00000	1781.00000	1247.00000	1635.00000	1198.00000	1618.00000	482.00000	1385.00000
mean	228541.04380	2178036.53577	143647.21524	114.41700	97.87706	2231.53473	4340.34511	10.08506	2.43466
std	410018.91819	1141709.89618	75298.75851	1755.26390	1533.05761	650.13959	8126.94081	44.78332	1.06519
min	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	2.22459	1.00000	1.00000
25%	90000.00000	1349453.60000	89000.00000	40.00000	38.00000	1815.48583	1973.75000	2.00000	2.00000
50%	130000.00000	1849812.80000	122000.00000	55.00000	50.00000	2151.64234	2411.23950	4.00000	2.00000
75%	191134.00000	2653420.00000	175000.00000	79.00000	73.00000	2577.00358	2958.15746	7.00000	3.00000
max	5487000.00000	6034635.20000	398000.00000	62034.00000	62034.00000	5975.60976	57156.25000	616.00000	6.00000

	operation	property_type	place_with_parent_names	price	currency	price_aprox_local_currency	price_aprox_usd	surface_total_in_m2	surface_covered_in_m2	price_usd_per_m2	price_per_m2	floor	rooms	expenses	properati_url	lat	lon
4	sell	apartment	\|Argentina\|Capital Federal\|Chacarita\|	129000.0	USD	1955949.6	129000.0	76.0	70.0	1697.368421	1842.857143	NaN	NaN	NaN	http://chacarita.properati.com.ar/10qlv_venta_...	-34.584651	-58.454693
9	sell	apartment	\|Argentina\|Capital Federal\|Villa Luro\|	87000.0	USD	1319128.8	87000.0	48.0	42.0	1812.500000	2071.428571	NaN	NaN	NaN	http://villa-luro.properati.com.ar/12m82_venta...	-34.638979	-58.500115
29	sell	apartment	\|Argentina\|Capital Federal\|Caballito\|	118000.0	USD	1789163.2	118000.0	NaN	54.0	NaN	2185.185185	NaN	2.0	NaN	http://caballito.properati.com.ar/11wqh_venta_...	-34.615847	-58.459957
40	sell	apartment	\|Argentina\|Capital Federal\|Constitución\|	57000.0	USD	864256.8	57000.0	42.0	42.0	1357.142857	1357.142857	5.0	2.0	364	http://constitucion.properati.com.ar/k2f0_vent...	-34.625222	-58.382382
41	sell	apartment	\|Argentina\|Capital Federal\|Once\|	90000.0	USD	1364616.0	90000.0	57.0	50.0	1578.947368	1800.000000	NaN	3.0	450	http://once.properati.com.ar/suwa_venta_depart...	-34.610610	-58.412511

	operation	property_type	place_with_parent_names	price	currency	price_aprox_local_currency	price_aprox_usd	surface_total_in_m2	surface_covered_in_m2	price_usd_per_m2	price_per_m2	floor	rooms	expenses	properati_url	lat	lon
4	sell	apartment	\|Argentina\|Capital Federal\|Chacarita\|	129000.0	USD	1955949.6	129000.0	76.0	70.0	1697.368421	1842.857143	NaN	NaN	NaN	http://chacarita.properati.com.ar/10qlv_venta_...	-34.584651	-58.454693
9	sell	apartment	\|Argentina\|Capital Federal\|Villa Luro\|	87000.0	USD	1319128.8	87000.0	48.0	42.0	1812.500000	2071.428571	NaN	NaN	NaN	http://villa-luro.properati.com.ar/12m82_venta...	-34.638979	-58.500115
29	sell	apartment	\|Argentina\|Capital Federal\|Caballito\|	118000.0	USD	1789163.2	118000.0	NaN	54.0	NaN	2185.185185	NaN	2.0	NaN	http://caballito.properati.com.ar/11wqh_venta_...	-34.615847	-58.459957
40	sell	apartment	\|Argentina\|Capital Federal\|Constitución\|	57000.0	USD	864256.8	57000.0	42.0	42.0	1357.142857	1357.142857	5.0	2.0	364	http://constitucion.properati.com.ar/k2f0_vent...	-34.625222	-58.382382
41	sell	apartment	\|Argentina\|Capital Federal\|Once\|	90000.0	USD	1364616.0	90000.0	57.0	50.0	1578.947368	1800.000000	NaN	3.0	450	http://once.properati.com.ar/suwa_venta_depart...	-34.610610	-58.412511

Preparing and Exploring the Data¶

Build Model¶

Baseline¶

Evaluating¶

Comunicating the Results¶

Feature Importance¶

	price	surface_covered_in_m2
neighborhood
Parque Chacabuco	597018.833333	69.000000
Barracas	491165.064516	54.387097
Boedo	453569.750000	50.250000
Saavedra	331669.558824	51.970588
San Cristobal	331016.729730	51.351351
Colegiales	317316.571429	57.380952
Belgrano	314852.141732	57.543307
San Telmo	298507.484848	54.757576
Nuñez	296303.395833	55.583333
Flores	287899.000000	57.500000
Centro / Microcentro	285826.100000	49.500000
Caballito	274527.260000	50.110000
Puerto Madero	263619.545455	56.090909
Constitución	248722.222222	51.777778
Las Cañitas	228626.875000	62.750000
Parque Centenario	217940.000000	48.700000
Recoleta	208590.800000	68.766667
Congreso	208125.000000	57.375000
Coghlan	194600.600000	40.300000
Barrio Norte	183231.763158	64.052632
Villa Luro	182166.875000	59.250000
Palermo	179831.396825	53.293651
Villa Urquiza	172493.882353	52.397059
Almagro	160323.044444	54.000000
Retiro	155400.000000	58.833333
Monserrat	143211.538462	49.384615
Balvanera	141458.622222	55.488889
Villa Crespo	140503.931818	54.000000
Chacarita	138555.555556	57.222222
Abasto	136000.000000	64.000000
Monte Castro	129316.666667	65.000000
Villa General Mitre	124000.000000	69.333333
Villa Devoto	120536.411765	53.882353
Tribunales	120000.000000	47.000000
Villa Pueyrredón	118163.333333	56.666667
Floresta	117876.470588	65.058824
Versalles	116000.000000	62.000000
Once	115275.000000	65.000000
Villa del Parque	113889.866667	54.466667
Paternal	113065.500000	50.812500
Parque Patricios	113033.333333	53.500000
San Nicolás	111692.307692	57.153846
Liniers	110714.285714	62.571429
Villa Ortuzar	104000.000000	44.500000
Parque Chas	103722.333333	45.333333
	102375.000000	45.812500
Agronomía	101250.000000	39.250000
Mataderos	99613.888889	54.888889
Boca	98666.666667	59.200000
Velez Sarsfield	92000.000000	52.000000
Parque Avellaneda	82400.000000	49.200000
Villa Santa Rita	81683.333333	43.000000
Villa Lugano	75533.333333	63.833333